Active Learning with Logged Data

This repository contains a python implementation of experiments in paper:

Songbai Yan, Kamalika Chaudhuri, Tara Javidi. "Active Learning with Logged Data." Proceedings of the 35th International Conference on Machine Learning, PMLR 80:5521-5530, 2018.

File Descriptions

• algos.py Implementation of active learning algorithms
• data.py Preprocessing of data sets
• experiments.py Implementation of experiments
• logger.py Logging information for plots and reports
• main.py The main file
• model.py Implementation of a linear model
• opt.py Implementation of stochastic gradient descent with weighted samples
• utils.py Auxiliary functions

Data sets should be put in "../data", and results are located in "../results/".